Wentworth
Institute of Technology
Mastering TensorFlow 1.x : Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras.
We cover advanced deep learning concepts (such as transfer learning, generative adversarial models, and reinforcement learning), and implement them using TensorFlow and Keras. We cover how to build and deploy at scale with distributed models. You will learn to build TensorFlow models using R, Keras,...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2018.
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| Subjects: | |
| Online Access: |
Full text (Wentworth users only) |
| Local Note: | ProQuest Ebook Central |
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| 100 | 1 | |a Fandango, Armando. | |
| 245 | 1 | 0 | |a Mastering TensorFlow 1.x : |b Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (464 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 500 | |a Keras-based MLP for MNIST classification. | ||
| 505 | 0 | |a Cover; Copyright and Credits; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: TensorFlow 101; What is TensorFlow?; TensorFlow core; Code warm-up -- Hello TensorFlow; Tensors; Constants; Operations; Placeholders; Creating tensors from Python objects; Variables; Tensors generated from library functions; Populating tensor elements with the same values; Populating tensor elements with sequences; Populating tensor elements with a random distribution; Getting Variables with tf.get_variable(); Data flow graph or computation graph; Order of execution and lazy loading. | |
| 505 | 8 | |a Executing graphs across compute devices -- CPU and GPGPUPlacing graph nodes on specific compute devices; Simple placement; Dynamic placement; Soft placement; GPU memory handling; Multiple graphs; TensorBoard; A TensorBoard minimal example; TensorBoard details; Summary; Chapter 2: High-Level Libraries for TensorFlow; TF Estimator -- previously TF Learn; TF Slim; TFLearn; Creating the TFLearn Layers; TFLearn core layers; TFLearn convolutional layers; TFLearn recurrent layers; TFLearn normalization layers; TFLearn embedding layers; TFLearn merge layers; TFLearn estimator layers. | |
| 505 | 8 | |a Creating the TFLearn ModelTypes of TFLearn models; Training the TFLearn Model; Using the TFLearn Model; PrettyTensor; Sonnet; Summary; Chapter 3: Keras 101; Installing Keras; Neural Network Models in Keras; Workflow for building models in Keras; Creating the Keras model; Sequential API for creating the Keras model; Functional API for creating the Keras model; Keras Layers; Keras core layers; Keras convolutional layers; Keras pooling layers; Keras locally-connected layers; Keras recurrent layers; Keras embedding layers; Keras merge layers; Keras advanced activation layers. | |
| 505 | 8 | |a Keras normalization layersKeras noise layers; Adding Layers to the Keras Model; Sequential API to add layers to the Keras model; Functional API to add layers to the Keras Model; Compiling the Keras model; Training the Keras model; Predicting with the Keras model; Additional modules in Keras; Keras sequential model example for MNIST dataset; Summary; Chapter 4: Classical Machine Learning with TensorFlow; Simple linear regression; Data preparation; Building a simple regression model; Defining the inputs, parameters, and other variables; Defining the model; Defining the loss function. | |
| 505 | 8 | |a Defining the optimizer functionTraining the model; Using the trained model to predict; Multi-regression; Regularized regression; Lasso regularization; Ridge regularization; ElasticNet regularization; Classification using logistic regression; Logistic regression for binary classification; Logistic regression for multiclass classification; Binary classification; Multiclass classification; Summary; Chapter 5: Neural Networks and MLP with TensorFlow and Keras; The perceptron; MultiLayer Perceptron; MLP for image classification; TensorFlow-based MLP for MNIST classification. | |
| 520 | |a We cover advanced deep learning concepts (such as transfer learning, generative adversarial models, and reinforcement learning), and implement them using TensorFlow and Keras. We cover how to build and deploy at scale with distributed models. You will learn to build TensorFlow models using R, Keras, TensorFlow Learn, TensorFlow Slim and Sonnet. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 700 | 1 | |a McClure, Nick. | |
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| 776 | 0 | 8 | |i Print version: |a Fandango, Armando. |t Mastering TensorFlow 1.x : Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras. |d Birmingham : Packt Publishing, ©2018 |
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